Overview

Dataset statistics

Number of variables28
Number of observations2240
Missing cells24
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory503.4 B

Variable types

Numeric14
Categorical14

Warnings

Income has a high cardinality: 1974 distinct values High cardinality
Dt_Customer has a high cardinality: 663 distinct values High cardinality
Kidhome is highly correlated with NumCatalogPurchasesHigh correlation
MntWines is highly correlated with MntMeatProducts and 3 other fieldsHigh correlation
MntFruits is highly correlated with MntMeatProducts and 2 other fieldsHigh correlation
MntMeatProducts is highly correlated with MntWines and 5 other fieldsHigh correlation
MntFishProducts is highly correlated with MntFruits and 3 other fieldsHigh correlation
MntSweetProducts is highly correlated with MntFruits and 2 other fieldsHigh correlation
NumWebPurchases is highly correlated with MntWines and 1 other fieldsHigh correlation
NumCatalogPurchases is highly correlated with Kidhome and 5 other fieldsHigh correlation
NumStorePurchases is highly correlated with MntWines and 2 other fieldsHigh correlation
NumWebVisitsMonth is highly correlated with MntMeatProducts and 1 other fieldsHigh correlation
Kidhome is highly correlated with MntWines and 3 other fieldsHigh correlation
MntWines is highly correlated with Kidhome and 8 other fieldsHigh correlation
MntFruits is highly correlated with MntWines and 6 other fieldsHigh correlation
MntMeatProducts is highly correlated with Kidhome and 8 other fieldsHigh correlation
MntFishProducts is highly correlated with MntWines and 6 other fieldsHigh correlation
MntSweetProducts is highly correlated with MntWines and 6 other fieldsHigh correlation
MntGoldProds is highly correlated with MntWines and 7 other fieldsHigh correlation
NumWebPurchases is highly correlated with MntWines and 4 other fieldsHigh correlation
NumCatalogPurchases is highly correlated with Kidhome and 9 other fieldsHigh correlation
NumStorePurchases is highly correlated with Kidhome and 8 other fieldsHigh correlation
NumWebVisitsMonth is highly correlated with NumCatalogPurchasesHigh correlation
Kidhome is highly correlated with NumCatalogPurchasesHigh correlation
MntWines is highly correlated with MntMeatProducts and 3 other fieldsHigh correlation
MntFruits is highly correlated with MntMeatProducts and 2 other fieldsHigh correlation
MntMeatProducts is highly correlated with MntWines and 6 other fieldsHigh correlation
MntFishProducts is highly correlated with MntFruits and 3 other fieldsHigh correlation
MntSweetProducts is highly correlated with MntFruits and 2 other fieldsHigh correlation
NumWebPurchases is highly correlated with MntWines and 2 other fieldsHigh correlation
NumCatalogPurchases is highly correlated with Kidhome and 4 other fieldsHigh correlation
NumStorePurchases is highly correlated with MntWines and 3 other fieldsHigh correlation
AcceptedCmp1 is highly correlated with AcceptedCmp5High correlation
Kidhome is highly correlated with NumStorePurchases and 3 other fieldsHigh correlation
NumStorePurchases is highly correlated with Kidhome and 9 other fieldsHigh correlation
NumCatalogPurchases is highly correlated with Kidhome and 4 other fieldsHigh correlation
NumWebVisitsMonth is highly correlated with NumStorePurchases and 2 other fieldsHigh correlation
MntFishProducts is highly correlated with NumStorePurchases and 5 other fieldsHigh correlation
MntWines is highly correlated with Kidhome and 8 other fieldsHigh correlation
AcceptedCmp4 is highly correlated with MntWinesHigh correlation
NumDealsPurchases is highly correlated with NumStorePurchases and 2 other fieldsHigh correlation
Teenhome is highly correlated with NumDealsPurchasesHigh correlation
MntFruits is highly correlated with NumStorePurchases and 3 other fieldsHigh correlation
MntMeatProducts is highly correlated with NumStorePurchases and 2 other fieldsHigh correlation
MntSweetProducts is highly correlated with Kidhome and 4 other fieldsHigh correlation
MntGoldProds is highly correlated with NumStorePurchases and 4 other fieldsHigh correlation
AcceptedCmp5 is highly correlated with AcceptedCmp1 and 1 other fieldsHigh correlation
NumWebPurchases is highly correlated with NumStorePurchases and 3 other fieldsHigh correlation
Income has 24 (1.1%) missing values Missing
Income is uniformly distributed Uniform
ID has unique values Unique
Recency has 28 (1.2%) zeros Zeros
MntFruits has 400 (17.9%) zeros Zeros
MntFishProducts has 384 (17.1%) zeros Zeros
MntSweetProducts has 419 (18.7%) zeros Zeros
MntGoldProds has 61 (2.7%) zeros Zeros
NumDealsPurchases has 46 (2.1%) zeros Zeros
NumWebPurchases has 49 (2.2%) zeros Zeros
NumCatalogPurchases has 586 (26.2%) zeros Zeros

Reproduction

Analysis started2021-06-01 22:38:56.579984
Analysis finished2021-06-01 22:40:14.339284
Duration1 minute and 17.76 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

ID
Real number (ℝ≥0)

UNIQUE

Distinct2240
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5592.159821
Minimum0
Maximum11191
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-06-02T04:10:14.608519image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile576.85
Q12828.25
median5458.5
Q38427.75
95-th percentile10675.05
Maximum11191
Range11191
Interquartile range (IQR)5599.5

Descriptive statistics

Standard deviation3246.662198
Coefficient of variation (CV)0.5805739287
Kurtosis-1.190028038
Mean5592.159821
Median Absolute Deviation (MAD)2791
Skewness0.0398318728
Sum12526438
Variance10540815.43
MonotonicityNot monotonic
2021-06-02T04:10:14.888649image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40961
 
< 0.1%
109141
 
< 0.1%
6631
 
< 0.1%
27121
 
< 0.1%
109061
 
< 0.1%
28311
 
< 0.1%
47671
 
< 0.1%
47691
 
< 0.1%
6751
 
< 0.1%
67981
 
< 0.1%
Other values (2230)2230
99.6%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
91
< 0.1%
131
< 0.1%
171
< 0.1%
201
< 0.1%
221
< 0.1%
241
< 0.1%
251
< 0.1%
351
< 0.1%
ValueCountFrequency (%)
111911
< 0.1%
111881
< 0.1%
111871
< 0.1%
111811
< 0.1%
111781
< 0.1%
111761
< 0.1%
111711
< 0.1%
111661
< 0.1%
111481
< 0.1%
111331
< 0.1%

Year_Birth
Real number (ℝ≥0)

Distinct59
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1968.805804
Minimum1893
Maximum1996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-06-02T04:10:15.178700image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1893
5-th percentile1950
Q11959
median1970
Q31977
95-th percentile1988
Maximum1996
Range103
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.98406946
Coefficient of variation (CV)0.006086973858
Kurtosis0.7174644425
Mean1968.805804
Median Absolute Deviation (MAD)9
Skewness-0.3499438592
Sum4410125
Variance143.6179207
MonotonicityNot monotonic
2021-06-02T04:10:15.516887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
197689
 
4.0%
197187
 
3.9%
197583
 
3.7%
197279
 
3.5%
197077
 
3.4%
197877
 
3.4%
197374
 
3.3%
196574
 
3.3%
196971
 
3.2%
197469
 
3.1%
Other values (49)1460
65.2%
ValueCountFrequency (%)
18931
 
< 0.1%
18991
 
< 0.1%
19001
 
< 0.1%
19401
 
< 0.1%
19411
 
< 0.1%
19437
0.3%
19447
0.3%
19458
0.4%
194616
0.7%
194716
0.7%
ValueCountFrequency (%)
19962
 
0.1%
19955
 
0.2%
19943
 
0.1%
19935
 
0.2%
199213
0.6%
199115
0.7%
199018
0.8%
198930
1.3%
198829
1.3%
198727
1.2%

Education
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size141.3 KiB
Graduation
1127 
PhD
486 
Master
370 
2n Cycle
203 
Basic
 
54

Length

Max length10
Median length10
Mean length7.51875
Min length3

Characters and Unicode

Total characters16842
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGraduation
2nd rowGraduation
3rd rowGraduation
4th rowGraduation
5th rowGraduation

Common Values

ValueCountFrequency (%)
Graduation1127
50.3%
PhD486
21.7%
Master370
 
16.5%
2n Cycle203
 
9.1%
Basic54
 
2.4%

Length

2021-06-02T04:10:16.118897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-02T04:10:16.321877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
graduation1127
46.1%
phd486
19.9%
master370
 
15.1%
2n203
 
8.3%
cycle203
 
8.3%
basic54
 
2.2%

Most occurring characters

ValueCountFrequency (%)
a2678
15.9%
r1497
8.9%
t1497
8.9%
n1330
 
7.9%
i1181
 
7.0%
G1127
 
6.7%
d1127
 
6.7%
u1127
 
6.7%
o1127
 
6.7%
e573
 
3.4%
Other values (12)3578
21.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13710
81.4%
Uppercase Letter2726
 
16.2%
Decimal Number203
 
1.2%
Space Separator203
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a2678
19.5%
r1497
10.9%
t1497
10.9%
n1330
9.7%
i1181
8.6%
d1127
8.2%
u1127
8.2%
o1127
8.2%
e573
 
4.2%
h486
 
3.5%
Other values (4)1087
7.9%
Uppercase Letter
ValueCountFrequency (%)
G1127
41.3%
P486
17.8%
D486
17.8%
M370
 
13.6%
C203
 
7.4%
B54
 
2.0%
Decimal Number
ValueCountFrequency (%)
2203
100.0%
Space Separator
ValueCountFrequency (%)
203
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin16436
97.6%
Common406
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a2678
16.3%
r1497
9.1%
t1497
9.1%
n1330
8.1%
i1181
 
7.2%
G1127
 
6.9%
d1127
 
6.9%
u1127
 
6.9%
o1127
 
6.9%
e573
 
3.5%
Other values (10)3172
19.3%
Common
ValueCountFrequency (%)
2203
50.0%
203
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII16842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a2678
15.9%
r1497
8.9%
t1497
8.9%
n1330
 
7.9%
i1181
 
7.0%
G1127
 
6.7%
d1127
 
6.7%
u1127
 
6.7%
o1127
 
6.7%
e573
 
3.4%
Other values (12)3578
21.2%

Marital_Status
Categorical

Distinct8
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size140.3 KiB
Married
864 
Together
580 
Single
480 
Divorced
232 
Widow
 
77
Other values (3)
 
7

Length

Max length8
Median length7
Mean length7.073214286
Min length4

Characters and Unicode

Total characters15844
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDivorced
2nd rowSingle
3rd rowMarried
4th rowTogether
5th rowSingle

Common Values

ValueCountFrequency (%)
Married864
38.6%
Together580
25.9%
Single480
21.4%
Divorced232
 
10.4%
Widow77
 
3.4%
Alone3
 
0.1%
Absurd2
 
0.1%
YOLO2
 
0.1%

Length

2021-06-02T04:10:16.878957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-02T04:10:17.133031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
married864
38.6%
together580
25.9%
single480
21.4%
divorced232
 
10.4%
widow77
 
3.4%
alone3
 
0.1%
yolo2
 
0.1%
absurd2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e2739
17.3%
r2542
16.0%
i1653
10.4%
d1175
7.4%
g1060
 
6.7%
o892
 
5.6%
M864
 
5.5%
a864
 
5.5%
T580
 
3.7%
t580
 
3.7%
Other values (16)2895
18.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13598
85.8%
Uppercase Letter2246
 
14.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2739
20.1%
r2542
18.7%
i1653
12.2%
d1175
8.6%
g1060
 
7.8%
o892
 
6.6%
a864
 
6.4%
t580
 
4.3%
h580
 
4.3%
n483
 
3.6%
Other values (7)1030
 
7.6%
Uppercase Letter
ValueCountFrequency (%)
M864
38.5%
T580
25.8%
S480
21.4%
D232
 
10.3%
W77
 
3.4%
A5
 
0.2%
O4
 
0.2%
Y2
 
0.1%
L2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin15844
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2739
17.3%
r2542
16.0%
i1653
10.4%
d1175
7.4%
g1060
 
6.7%
o892
 
5.6%
M864
 
5.5%
a864
 
5.5%
T580
 
3.7%
t580
 
3.7%
Other values (16)2895
18.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII15844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e2739
17.3%
r2542
16.0%
i1653
10.4%
d1175
7.4%
g1060
 
6.7%
o892
 
5.6%
M864
 
5.5%
a864
 
5.5%
T580
 
3.7%
t580
 
3.7%
Other values (16)2895
18.3%

Income
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct1974
Distinct (%)89.1%
Missing24
Missing (%)1.1%
Memory size148.0 KiB
$7,500.00
 
12
$35,860.00
 
4
$34,176.00
 
3
$63,841.00
 
3
$80,134.00
 
3
Other values (1969)
2191 

Length

Max length12
Median length11
Mean length10.99277978
Min length10

Characters and Unicode

Total characters24360
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1756 ?
Unique (%)79.2%

Sample

1st row$84,835.00
2nd row$57,091.00
3rd row$67,267.00
4th row$32,474.00
5th row$21,474.00

Common Values

ValueCountFrequency (%)
$7,500.00 12
 
0.5%
$35,860.00 4
 
0.2%
$34,176.00 3
 
0.1%
$63,841.00 3
 
0.1%
$80,134.00 3
 
0.1%
$37,760.00 3
 
0.1%
$83,844.00 3
 
0.1%
$67,445.00 3
 
0.1%
$48,432.00 3
 
0.1%
$39,922.00 3
 
0.1%
Other values (1964)2176
97.1%
(Missing)24
 
1.1%

Length

2021-06-02T04:10:17.909427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7,500.0012
 
0.5%
35,860.004
 
0.2%
63,841.003
 
0.1%
80,134.003
 
0.1%
46,098.003
 
0.1%
83,844.003
 
0.1%
39,922.003
 
0.1%
18,929.003
 
0.1%
18,690.003
 
0.1%
34,176.003
 
0.1%
Other values (1964)2176
98.2%

Most occurring characters

ValueCountFrequency (%)
05381
22.1%
$2216
9.1%
,2216
9.1%
.2216
9.1%
2216
9.1%
31244
 
5.1%
41235
 
5.1%
61213
 
5.0%
51204
 
4.9%
71156
 
4.7%
Other values (4)4063
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15496
63.6%
Other Punctuation4432
 
18.2%
Currency Symbol2216
 
9.1%
Space Separator2216
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05381
34.7%
31244
 
8.0%
41235
 
8.0%
61213
 
7.8%
51204
 
7.8%
71156
 
7.5%
21142
 
7.4%
81031
 
6.7%
1998
 
6.4%
9892
 
5.8%
Other Punctuation
ValueCountFrequency (%)
,2216
50.0%
.2216
50.0%
Currency Symbol
ValueCountFrequency (%)
$2216
100.0%
Space Separator
ValueCountFrequency (%)
2216
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common24360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05381
22.1%
$2216
9.1%
,2216
9.1%
.2216
9.1%
2216
9.1%
31244
 
5.1%
41235
 
5.1%
61213
 
5.0%
51204
 
4.9%
71156
 
4.7%
Other values (4)4063
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII24360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05381
22.1%
$2216
9.1%
,2216
9.1%
.2216
9.1%
2216
9.1%
31244
 
5.1%
41235
 
5.1%
61213
 
5.0%
51204
 
4.9%
71156
 
4.7%
Other values (4)4063
16.7%

Kidhome
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size127.0 KiB
0
1293 
1
899 
2
 
48

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
01293
57.7%
1899
40.1%
248
 
2.1%

Length

2021-06-02T04:10:18.428895image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-02T04:10:18.571467image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
01293
57.7%
1899
40.1%
248
 
2.1%

Most occurring characters

ValueCountFrequency (%)
01293
57.7%
1899
40.1%
248
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01293
57.7%
1899
40.1%
248
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01293
57.7%
1899
40.1%
248
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01293
57.7%
1899
40.1%
248
 
2.1%

Teenhome
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size127.0 KiB
0
1158 
1
1030 
2
 
52

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
01158
51.7%
11030
46.0%
252
 
2.3%

Length

2021-06-02T04:10:19.113122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-02T04:10:19.304788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
01158
51.7%
11030
46.0%
252
 
2.3%

Most occurring characters

ValueCountFrequency (%)
01158
51.7%
11030
46.0%
252
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01158
51.7%
11030
46.0%
252
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01158
51.7%
11030
46.0%
252
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01158
51.7%
11030
46.0%
252
 
2.3%

Dt_Customer
Categorical

HIGH CARDINALITY

Distinct663
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Memory size140.0 KiB
8/31/12
 
12
9/12/12
 
11
5/12/14
 
11
2/14/13
 
11
8/20/13
 
10
Other values (658)
2185 

Length

Max length8
Median length7
Mean length6.965625
Min length6

Characters and Unicode

Total characters15603
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique107 ?
Unique (%)4.8%

Sample

1st row6/16/14
2nd row6/15/14
3rd row5/13/14
4th row5/11/14
5th row4/8/14

Common Values

ValueCountFrequency (%)
8/31/1212
 
0.5%
9/12/1211
 
0.5%
5/12/1411
 
0.5%
2/14/1311
 
0.5%
8/20/1310
 
0.4%
5/22/1410
 
0.4%
3/23/149
 
0.4%
4/5/149
 
0.4%
10/29/129
 
0.4%
3/1/149
 
0.4%
Other values (653)2139
95.5%

Length

2021-06-02T04:10:20.444842image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8/31/1212
 
0.5%
9/12/1211
 
0.5%
5/12/1411
 
0.5%
2/14/1311
 
0.5%
8/20/1310
 
0.4%
5/22/1410
 
0.4%
3/23/149
 
0.4%
4/5/149
 
0.4%
10/29/129
 
0.4%
3/1/149
 
0.4%
Other values (653)2139
95.5%

Most occurring characters

ValueCountFrequency (%)
/4480
28.7%
14228
27.1%
21836
11.8%
31742
 
11.2%
4928
 
5.9%
8445
 
2.9%
5424
 
2.7%
0423
 
2.7%
9403
 
2.6%
6363
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11123
71.3%
Other Punctuation4480
28.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
14228
38.0%
21836
16.5%
31742
15.7%
4928
 
8.3%
8445
 
4.0%
5424
 
3.8%
0423
 
3.8%
9403
 
3.6%
6363
 
3.3%
7331
 
3.0%
Other Punctuation
ValueCountFrequency (%)
/4480
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/4480
28.7%
14228
27.1%
21836
11.8%
31742
 
11.2%
4928
 
5.9%
8445
 
2.9%
5424
 
2.7%
0423
 
2.7%
9403
 
2.6%
6363
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII15603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/4480
28.7%
14228
27.1%
21836
11.8%
31742
 
11.2%
4928
 
5.9%
8445
 
2.9%
5424
 
2.7%
0423
 
2.7%
9403
 
2.6%
6363
 
2.3%

Recency
Real number (ℝ≥0)

ZEROS

Distinct100
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.109375
Minimum0
Maximum99
Zeros28
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-06-02T04:10:20.711195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q124
median49
Q374
95-th percentile94
Maximum99
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.96245281
Coefficient of variation (CV)0.5897540502
Kurtosis-1.201896799
Mean49.109375
Median Absolute Deviation (MAD)25
Skewness-0.001986658634
Sum110005
Variance838.8236727
MonotonicityIncreasing
2021-06-02T04:10:20.985052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5637
 
1.7%
3032
 
1.4%
5432
 
1.4%
4631
 
1.4%
4930
 
1.3%
6530
 
1.3%
9230
 
1.3%
329
 
1.3%
7129
 
1.3%
2929
 
1.3%
Other values (90)1931
86.2%
ValueCountFrequency (%)
028
1.2%
124
1.1%
228
1.2%
329
1.3%
427
1.2%
515
0.7%
621
0.9%
712
0.5%
825
1.1%
924
1.1%
ValueCountFrequency (%)
9917
0.8%
9822
1.0%
9720
0.9%
9625
1.1%
9519
0.8%
9426
1.2%
9321
0.9%
9230
1.3%
9118
0.8%
9020
0.9%

MntWines
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct776
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean303.9357143
Minimum0
Maximum1493
Zeros13
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-06-02T04:10:21.259246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q123.75
median173.5
Q3504.25
95-th percentile1000
Maximum1493
Range1493
Interquartile range (IQR)480.5

Descriptive statistics

Standard deviation336.5973926
Coefficient of variation (CV)1.107462456
Kurtosis0.5987435935
Mean303.9357143
Median Absolute Deviation (MAD)164.5
Skewness1.175770564
Sum680816
Variance113297.8047
MonotonicityNot monotonic
2021-06-02T04:10:21.555774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
242
 
1.9%
540
 
1.8%
637
 
1.7%
137
 
1.7%
433
 
1.5%
830
 
1.3%
330
 
1.3%
928
 
1.2%
1225
 
1.1%
1024
 
1.1%
Other values (766)1914
85.4%
ValueCountFrequency (%)
013
 
0.6%
137
1.7%
242
1.9%
330
1.3%
433
1.5%
540
1.8%
637
1.7%
722
1.0%
830
1.3%
928
1.2%
ValueCountFrequency (%)
14931
< 0.1%
14922
0.1%
14861
< 0.1%
14782
0.1%
14621
< 0.1%
14591
< 0.1%
14491
< 0.1%
13961
< 0.1%
13941
< 0.1%
13791
< 0.1%

MntFruits
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct158
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.30223214
Minimum0
Maximum199
Zeros400
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-06-02T04:10:21.859736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q333
95-th percentile123
Maximum199
Range199
Interquartile range (IQR)32

Descriptive statistics

Standard deviation39.77343376
Coefficient of variation (CV)1.51216952
Kurtosis4.050976251
Mean26.30223214
Median Absolute Deviation (MAD)8
Skewness2.102063305
Sum58917
Variance1581.926033
MonotonicityNot monotonic
2021-06-02T04:10:22.169304image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0400
 
17.9%
1162
 
7.2%
2120
 
5.4%
3116
 
5.2%
4104
 
4.6%
767
 
3.0%
565
 
2.9%
662
 
2.8%
1250
 
2.2%
848
 
2.1%
Other values (148)1046
46.7%
ValueCountFrequency (%)
0400
17.9%
1162
7.2%
2120
 
5.4%
3116
 
5.2%
4104
 
4.6%
565
 
2.9%
662
 
2.8%
767
 
3.0%
848
 
2.1%
935
 
1.6%
ValueCountFrequency (%)
1992
0.1%
1971
 
< 0.1%
1943
0.1%
1932
0.1%
1901
 
< 0.1%
1891
 
< 0.1%
1852
0.1%
1841
 
< 0.1%
1833
0.1%
1811
 
< 0.1%

MntMeatProducts
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct558
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.95
Minimum0
Maximum1725
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-06-02T04:10:22.497689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q116
median67
Q3232
95-th percentile687.1
Maximum1725
Range1725
Interquartile range (IQR)216

Descriptive statistics

Standard deviation225.7153725
Coefficient of variation (CV)1.351993846
Kurtosis5.516724101
Mean166.95
Median Absolute Deviation (MAD)59
Skewness2.083233113
Sum373968
Variance50947.42939
MonotonicityNot monotonic
2021-06-02T04:10:22.736467image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
753
 
2.4%
550
 
2.2%
1149
 
2.2%
846
 
2.1%
643
 
1.9%
340
 
1.8%
1040
 
1.8%
938
 
1.7%
1636
 
1.6%
1235
 
1.6%
Other values (548)1810
80.8%
ValueCountFrequency (%)
01
 
< 0.1%
114
 
0.6%
230
1.3%
340
1.8%
430
1.3%
550
2.2%
643
1.9%
753
2.4%
846
2.1%
938
1.7%
ValueCountFrequency (%)
17252
0.1%
16221
< 0.1%
16071
< 0.1%
15821
< 0.1%
9841
< 0.1%
9811
< 0.1%
9741
< 0.1%
9681
< 0.1%
9611
< 0.1%
9512
0.1%

MntFishProducts
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct182
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.52544643
Minimum0
Maximum259
Zeros384
Zeros (%)17.1%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-06-02T04:10:23.035524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median12
Q350
95-th percentile168.05
Maximum259
Range259
Interquartile range (IQR)47

Descriptive statistics

Standard deviation54.6289794
Coefficient of variation (CV)1.45578493
Kurtosis3.096460912
Mean37.52544643
Median Absolute Deviation (MAD)12
Skewness1.919768971
Sum84057
Variance2984.325391
MonotonicityNot monotonic
2021-06-02T04:10:23.313996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0384
 
17.1%
2156
 
7.0%
3130
 
5.8%
4108
 
4.8%
682
 
3.7%
766
 
2.9%
858
 
2.6%
1055
 
2.5%
1348
 
2.1%
1247
 
2.1%
Other values (172)1106
49.4%
ValueCountFrequency (%)
0384
17.1%
110
 
0.4%
2156
7.0%
3130
 
5.8%
4108
 
4.8%
51
 
< 0.1%
682
 
3.7%
766
 
2.9%
858
 
2.6%
1055
 
2.5%
ValueCountFrequency (%)
2591
 
< 0.1%
2583
0.1%
2541
 
< 0.1%
2531
 
< 0.1%
2503
0.1%
2471
 
< 0.1%
2461
 
< 0.1%
2421
 
< 0.1%
2402
0.1%
2372
0.1%

MntSweetProducts
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct177
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.06294643
Minimum0
Maximum263
Zeros419
Zeros (%)18.7%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-06-02T04:10:23.601897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q333
95-th percentile126
Maximum263
Range263
Interquartile range (IQR)32

Descriptive statistics

Standard deviation41.28049849
Coefficient of variation (CV)1.525351225
Kurtosis4.376548261
Mean27.06294643
Median Absolute Deviation (MAD)8
Skewness2.136080712
Sum60621
Variance1704.079555
MonotonicityNot monotonic
2021-06-02T04:10:23.889680image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0419
 
18.7%
1161
 
7.2%
2128
 
5.7%
3101
 
4.5%
482
 
3.7%
565
 
2.9%
664
 
2.9%
757
 
2.5%
856
 
2.5%
1245
 
2.0%
Other values (167)1062
47.4%
ValueCountFrequency (%)
0419
18.7%
1161
 
7.2%
2128
 
5.7%
3101
 
4.5%
482
 
3.7%
565
 
2.9%
664
 
2.9%
757
 
2.5%
856
 
2.5%
942
 
1.9%
ValueCountFrequency (%)
2631
 
< 0.1%
2621
 
< 0.1%
1981
 
< 0.1%
1971
 
< 0.1%
1961
 
< 0.1%
1951
 
< 0.1%
1943
0.1%
1923
0.1%
1911
 
< 0.1%
1892
0.1%

MntGoldProds
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct213
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.021875
Minimum0
Maximum362
Zeros61
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-06-02T04:10:24.166423image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median24
Q356
95-th percentile165.05
Maximum362
Range362
Interquartile range (IQR)47

Descriptive statistics

Standard deviation52.16743891
Coefficient of variation (CV)1.185034461
Kurtosis3.55170925
Mean44.021875
Median Absolute Deviation (MAD)18
Skewness1.886105609
Sum98609
Variance2721.441683
MonotonicityNot monotonic
2021-06-02T04:10:24.420578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
173
 
3.3%
470
 
3.1%
369
 
3.1%
563
 
2.8%
1263
 
2.8%
262
 
2.8%
061
 
2.7%
657
 
2.5%
754
 
2.4%
1049
 
2.2%
Other values (203)1619
72.3%
ValueCountFrequency (%)
061
2.7%
173
3.3%
262
2.8%
369
3.1%
470
3.1%
563
2.8%
657
2.5%
754
2.4%
840
1.8%
944
2.0%
ValueCountFrequency (%)
3621
< 0.1%
3211
< 0.1%
2911
< 0.1%
2621
< 0.1%
2491
< 0.1%
2481
< 0.1%
2471
< 0.1%
2461
< 0.1%
2451
< 0.1%
2422
0.1%

NumDealsPurchases
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.325
Minimum0
Maximum15
Zeros46
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-06-02T04:10:24.693533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.932237501
Coefficient of variation (CV)0.8310698928
Kurtosis8.936914321
Mean2.325
Median Absolute Deviation (MAD)1
Skewness2.418569388
Sum5208
Variance3.73354176
MonotonicityNot monotonic
2021-06-02T04:10:24.889181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1970
43.3%
2497
22.2%
3297
 
13.3%
4189
 
8.4%
594
 
4.2%
661
 
2.7%
046
 
2.1%
740
 
1.8%
814
 
0.6%
98
 
0.4%
Other values (5)24
 
1.1%
ValueCountFrequency (%)
046
 
2.1%
1970
43.3%
2497
22.2%
3297
 
13.3%
4189
 
8.4%
594
 
4.2%
661
 
2.7%
740
 
1.8%
814
 
0.6%
98
 
0.4%
ValueCountFrequency (%)
157
 
0.3%
133
 
0.1%
124
 
0.2%
115
 
0.2%
105
 
0.2%
98
 
0.4%
814
 
0.6%
740
1.8%
661
2.7%
594
4.2%

NumWebPurchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.084821429
Minimum0
Maximum27
Zeros49
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-06-02T04:10:25.113417image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile9
Maximum27
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.778714147
Coefficient of variation (CV)0.680253518
Kurtosis5.703128364
Mean4.084821429
Median Absolute Deviation (MAD)2
Skewness1.382794296
Sum9150
Variance7.721252313
MonotonicityNot monotonic
2021-06-02T04:10:25.357527image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2373
16.7%
1354
15.8%
3336
15.0%
4280
12.5%
5220
9.8%
6205
9.2%
7155
6.9%
8102
 
4.6%
975
 
3.3%
049
 
2.2%
Other values (5)91
 
4.1%
ValueCountFrequency (%)
049
 
2.2%
1354
15.8%
2373
16.7%
3336
15.0%
4280
12.5%
5220
9.8%
6205
9.2%
7155
6.9%
8102
 
4.6%
975
 
3.3%
ValueCountFrequency (%)
272
 
0.1%
251
 
< 0.1%
231
 
< 0.1%
1144
 
2.0%
1043
 
1.9%
975
 
3.3%
8102
4.6%
7155
6.9%
6205
9.2%
5220
9.8%

NumCatalogPurchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.662053571
Minimum0
Maximum28
Zeros586
Zeros (%)26.2%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-06-02T04:10:25.592447image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile9
Maximum28
Range28
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.923100656
Coefficient of variation (CV)1.098062296
Kurtosis8.047436789
Mean2.662053571
Median Absolute Deviation (MAD)2
Skewness1.880988778
Sum5963
Variance8.544517442
MonotonicityNot monotonic
2021-06-02T04:10:25.778212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0586
26.2%
1497
22.2%
2276
12.3%
3184
 
8.2%
4182
 
8.1%
5140
 
6.2%
6128
 
5.7%
779
 
3.5%
855
 
2.5%
1048
 
2.1%
Other values (4)65
 
2.9%
ValueCountFrequency (%)
0586
26.2%
1497
22.2%
2276
12.3%
3184
 
8.2%
4182
 
8.1%
5140
 
6.2%
6128
 
5.7%
779
 
3.5%
855
 
2.5%
942
 
1.9%
ValueCountFrequency (%)
283
 
0.1%
221
 
< 0.1%
1119
 
0.8%
1048
 
2.1%
942
 
1.9%
855
 
2.5%
779
3.5%
6128
5.7%
5140
6.2%
4182
8.1%

NumStorePurchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.790178571
Minimum0
Maximum13
Zeros15
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-06-02T04:10:25.995638image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median5
Q38
95-th percentile12
Maximum13
Range13
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.250958146
Coefficient of variation (CV)0.5614607746
Kurtosis-0.6220482771
Mean5.790178571
Median Absolute Deviation (MAD)2
Skewness0.7022372855
Sum12970
Variance10.56872886
MonotonicityNot monotonic
2021-06-02T04:10:26.262805image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3490
21.9%
4323
14.4%
2223
10.0%
5212
9.5%
6178
 
7.9%
8149
 
6.7%
7143
 
6.4%
10125
 
5.6%
9106
 
4.7%
12105
 
4.7%
Other values (4)186
 
8.3%
ValueCountFrequency (%)
015
 
0.7%
17
 
0.3%
2223
10.0%
3490
21.9%
4323
14.4%
5212
9.5%
6178
 
7.9%
7143
 
6.4%
8149
 
6.7%
9106
 
4.7%
ValueCountFrequency (%)
1383
 
3.7%
12105
 
4.7%
1181
 
3.6%
10125
 
5.6%
9106
 
4.7%
8149
6.7%
7143
6.4%
6178
7.9%
5212
9.5%
4323
14.4%

NumWebVisitsMonth
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct16
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.316517857
Minimum0
Maximum20
Zeros11
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-06-02T04:10:26.530214image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q37
95-th percentile8
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.42664501
Coefficient of variation (CV)0.4564350341
Kurtosis1.821613827
Mean5.316517857
Median Absolute Deviation (MAD)2
Skewness0.2079255568
Sum11909
Variance5.888606002
MonotonicityNot monotonic
2021-06-02T04:10:26.797129image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7393
17.5%
8342
15.3%
6340
15.2%
5281
12.5%
4218
9.7%
3205
9.2%
2202
9.0%
1153
 
6.8%
983
 
3.7%
011
 
0.5%
Other values (6)12
 
0.5%
ValueCountFrequency (%)
011
 
0.5%
1153
 
6.8%
2202
9.0%
3205
9.2%
4218
9.7%
5281
12.5%
6340
15.2%
7393
17.5%
8342
15.3%
983
 
3.7%
ValueCountFrequency (%)
203
 
0.1%
192
 
0.1%
171
 
< 0.1%
142
 
0.1%
131
 
< 0.1%
103
 
0.1%
983
 
3.7%
8342
15.3%
7393
17.5%
6340
15.2%

AcceptedCmp3
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size127.0 KiB
0
2077 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
02077
92.7%
1163
 
7.3%

Length

2021-06-02T04:10:27.318821image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-02T04:10:27.492317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
02077
92.7%
1163
 
7.3%

Most occurring characters

ValueCountFrequency (%)
02077
92.7%
1163
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02077
92.7%
1163
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02077
92.7%
1163
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02077
92.7%
1163
 
7.3%

AcceptedCmp4
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size127.0 KiB
0
2073 
1
 
167

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02073
92.5%
1167
 
7.5%

Length

2021-06-02T04:10:27.962745image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-02T04:10:28.126245image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
02073
92.5%
1167
 
7.5%

Most occurring characters

ValueCountFrequency (%)
02073
92.5%
1167
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02073
92.5%
1167
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
Common2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02073
92.5%
1167
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02073
92.5%
1167
 
7.5%

AcceptedCmp5
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size127.0 KiB
0
2077 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02077
92.7%
1163
 
7.3%

Length

2021-06-02T04:10:28.564635image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-02T04:10:28.709029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
02077
92.7%
1163
 
7.3%

Most occurring characters

ValueCountFrequency (%)
02077
92.7%
1163
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02077
92.7%
1163
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02077
92.7%
1163
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02077
92.7%
1163
 
7.3%

AcceptedCmp1
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size127.0 KiB
0
2096 
1
 
144

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02096
93.6%
1144
 
6.4%

Length

2021-06-02T04:10:29.156305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-02T04:10:29.302839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
02096
93.6%
1144
 
6.4%

Most occurring characters

ValueCountFrequency (%)
02096
93.6%
1144
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02096
93.6%
1144
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02096
93.6%
1144
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02096
93.6%
1144
 
6.4%

AcceptedCmp2
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size127.0 KiB
0
2210 
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02210
98.7%
130
 
1.3%

Length

2021-06-02T04:10:29.713745image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-02T04:10:29.867891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
02210
98.7%
130
 
1.3%

Most occurring characters

ValueCountFrequency (%)
02210
98.7%
130
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02210
98.7%
130
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02210
98.7%
130
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02210
98.7%
130
 
1.3%

Response
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size127.0 KiB
0
1906 
1
334 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
01906
85.1%
1334
 
14.9%

Length

2021-06-02T04:10:30.368199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-02T04:10:30.572246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
01906
85.1%
1334
 
14.9%

Most occurring characters

ValueCountFrequency (%)
01906
85.1%
1334
 
14.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01906
85.1%
1334
 
14.9%

Most occurring scripts

ValueCountFrequency (%)
Common2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01906
85.1%
1334
 
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01906
85.1%
1334
 
14.9%

Complain
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size127.0 KiB
0
2219 
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02219
99.1%
121
 
0.9%

Length

2021-06-02T04:10:30.962460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-02T04:10:31.106503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
02219
99.1%
121
 
0.9%

Most occurring characters

ValueCountFrequency (%)
02219
99.1%
121
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02219
99.1%
121
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02219
99.1%
121
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02219
99.1%
121
 
0.9%

Country
Categorical

Distinct8
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size129.6 KiB
SP
1095 
SA
337 
CA
268 
AUS
160 
IND
148 
Other values (3)
232 

Length

Max length3
Median length2
Mean length2.191071429
Min length2

Characters and Unicode

Total characters4908
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSP
2nd rowCA
3rd rowUS
4th rowAUS
5th rowSP

Common Values

ValueCountFrequency (%)
SP1095
48.9%
SA337
 
15.0%
CA268
 
12.0%
AUS160
 
7.1%
IND148
 
6.6%
GER120
 
5.4%
US109
 
4.9%
ME3
 
0.1%

Length

2021-06-02T04:10:31.529411image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-02T04:10:31.704247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
sp1095
48.9%
sa337
 
15.0%
ca268
 
12.0%
aus160
 
7.1%
ind148
 
6.6%
ger120
 
5.4%
us109
 
4.9%
me3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
S1701
34.7%
P1095
22.3%
A765
15.6%
U269
 
5.5%
C268
 
5.5%
I148
 
3.0%
N148
 
3.0%
D148
 
3.0%
E123
 
2.5%
G120
 
2.4%
Other values (2)123
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4908
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S1701
34.7%
P1095
22.3%
A765
15.6%
U269
 
5.5%
C268
 
5.5%
I148
 
3.0%
N148
 
3.0%
D148
 
3.0%
E123
 
2.5%
G120
 
2.4%
Other values (2)123
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Latin4908
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S1701
34.7%
P1095
22.3%
A765
15.6%
U269
 
5.5%
C268
 
5.5%
I148
 
3.0%
N148
 
3.0%
D148
 
3.0%
E123
 
2.5%
G120
 
2.4%
Other values (2)123
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII4908
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S1701
34.7%
P1095
22.3%
A765
15.6%
U269
 
5.5%
C268
 
5.5%
I148
 
3.0%
N148
 
3.0%
D148
 
3.0%
E123
 
2.5%
G120
 
2.4%
Other values (2)123
 
2.5%

Interactions

2021-06-02T04:09:09.349050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:09.649824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:10.122793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:10.374954image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:10.661581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:10.931348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:11.198406image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:11.446317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:11.693539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:12.103467image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:12.400505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:12.742723image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:13.109761image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:13.426094image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:13.730246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:13.989692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:14.286572image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:14.573784image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:14.860581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:15.187072image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:15.512444image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:15.829169image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:16.145094image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:16.496039image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:16.874970image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:17.248815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:17.518109image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:17.813174image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:18.151011image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:18.408253image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:18.692828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:18.977915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:19.516690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:19.863875image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T04:09:20.242089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-02T04:10:32.851922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-02T04:10:33.734454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-02T04:10:34.566322image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-06-02T04:10:35.486124image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

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A simple visualization of nullity by column.
2021-06-02T04:10:13.294156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
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The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWinesMntFruitsMntMeatProductsMntFishProductsMntSweetProductsMntGoldProdsNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ResponseComplainCountry
018261970GraduationDivorced$84,835.00006/16/140189104379111189218144610000010SP
111961GraduationSingle$57,091.00006/15/1404645647037173750000110CA
2104761958GraduationMarried$67,267.00015/13/140134115915230132520000000US
313861967GraduationTogether$32,474.00115/11/1401001000110270000000AUS
453711989GraduationSingle$21,474.00104/8/1406162411034231271000010SP
573481958PhDSingle$71,691.00003/17/1403361304112403243147520000010SP
6407319542n CycleMarried$63,564.00001/29/1407698025215346511010761000010GER
719911967GraduationTogether$44,931.00011/18/14078011007121350000000SP
840471954PhDMarried$65,324.00011/11/140384010221325362940000000US
994771954PhDMarried$65,324.00011/11/140384010221325362940000000IND

Last rows

IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWinesMntFruitsMntMeatProductsMntFishProductsMntSweetProductsMntGoldProdsNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ResponseComplainCountry
2230336319742n CycleMarried$20,130.00003/17/14990637612110380000000SP
223185951973GraduationWidow$42,429.00012/11/14995506204211350000000AUS
223272321973GraduationWidow$42,429.00012/11/14995506204211350000000SP
2233782919002n CycleDivorced$36,640.00109/26/139915687425121250000001IND
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